Einstein Engagement Scoring is designed to predict the likelihood that an individual subscriber will engage with future email communications. Salesforce states that the model predicts email opens and clicks, subscriber retention, web conversion, and overall engagement for individual consumer contacts.

Those predicted likelihood values can be used to identify customers with low engagement propensity. A marketer can therefore segment, filter, suppress, or branch customers according to their predicted open or click probability before an audience flow sends a message.

Send Time Optimization solves a different problem. STO assumes that a recipient is going to receive the communication and predicts when that person is most likely to engage. It doesn't primarily determine whether a low-propensity customer should be excluded. Messaging Insights isn't the predictive subscriber-level engagement scoring capability described in this requirement.

Salesforce also stores the output in the Email Engagement Score DMO , which supports reporting and audience analysis based on open, click, and subscription likelihood.

The uploaded master bank correctly keys A. Salesforce's current Einstein Engagement Scoring model card confirms that it predicts opens, clicks, retention, and overall engagement.

Study Guide Reference: Agentforce and AI Innovation → Einstein Engagement Scoring → Predictive Audience Optimization.

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